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An intense level of academic pressure causes students to experience stress, and if this stress is not addressed, it can cause adverse mental and physical effects. Since the pandemic situation, students have received more assignments and other tasks due to the shift of classes to an online mode. Students may not realize that they are stressed, but it may be evident from other factors, including sleep deprivation and changes in eating habits. In this context, this paper presents a novel ensemble learning approach that proposes an architecture for stress level classification. It analyzes certain factors such as the sleep hours, productive time periods, screen time, weekly assignments and their submission statuses, and the studying methodology that contribute to stress among the students by collecting a survey from the student community. The survey data are preprocessed to categorize stress levels into three categories: highly stressed, manageable stress, and no stress. For the analysis of the minority class, oversampling methodology is used to remove the imbalance in the dataset, and decision tree, random forest classifier, AdaBoost, gradient boost, and ensemble learning algorithms with various combinations are implemented. To assess the model's performance, different metrics were used, such as the confusion matrix, accuracy, precision, recall, and F1 score. The results showed that the efficient ensemble learning academic stress classifier gave an accuracy of 93.48% and an F1 score of 93.14%. Fivefold cross-validation was also performed, and an accuracy of 93.45% was achieved. The receiver operating characteristic curve (ROC) value gave an accuracy of 98% for the no-stress category, while providing a 91% true positive rate for manageable and high-stress classes. The proposed ensemble learning with fivefold cross-validation outperformed various state-of-the-art algorithms to predict the stress level accurately. By using these results, students can identify areas for improvement, thereby reducing their stress levels and altering their academic lifestyles, thereby making our stress prediction approach more effective. [ABSTRACT FROM AUTHOR]

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a "Mental Health Protector" application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system's prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis. [ABSTRACT FROM AUTHOR]

In today's world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction. [ABSTRACT FROM AUTHOR]

Background: Stroke remains a major leading cause of morbidity and death globally. For ischemic stroke, the most frequent type of stroke, there are numerous risk models and risk assessments offered. Further research into potential risk factors or triggers is being sought to improve stroke risk models. Schizophrenia, bipolar disorder, and alcohol use disorder are all common causes of serious mental illnesses in the general population. Due to the tangled relationship between stroke and many chronic illnesses, lifestyle factors, and diet that may be present in a patient with a mental disease, the relationship between mental diseases and stroke requires further validation. Consequently, the purpose of this study is to assess the potential influence of bipolar disorder, schizophrenia, and alcohol use disorder on stroke patients as compared to non-stroke participants, after controlling for demographic, physical, and medical conditions. We aimed, as a secondary objective, to evaluate the impact of these pre-existing disorders on stroke severity levels. Methods: This research is a case-control survey study involving 113 Lebanese patients with a clinical diagnosis of ischemic stroke and 451 gender-matched volunteers without clinical signs of stroke as controls recruited from several hospitals in Lebanon (April 2020–April 2021). Based on the participant's consent, data was collected by filling out an anonymous paper-based questionnaire. Results: All of the odds ratios (ORs) generated by our regression model were greater than 1, indicating that the factors studied were associated with an increased risk of ischemic stroke. As such having schizophrenia (adjusted OR [aOR]: 6.162, 95% confidence interval [CI]: 1.136–33.423), bipolar disorder (aOR: 4.653, 95% CI: 1.214–17.834), alcohol use disorder (aOR: 3.918, 95% CI: 1.584–9.689), atrial fibrillation (aOR: 2.415, 95% CI: 1.235–4.721), diabetes (aOR: 1.865, 95% CI: 1.117–3.115), heart diseases (aOR: 9.890, 95% CI: 5.099–19.184), and asthma-COPD (aOR: 1.971, 95% CI: 1.190–3.263) were all involved with a high risk of developing an ischemic stroke. Moreover, obesity (aOR: 1.732, 95% CI: 1.049–2.861) and vigorous physical activity (aOR: 4.614, 95% CI: 2.669–7.978) were also linked to an increased risk of stroke. Moreover, our multinomial regression model revealed that the odds of moderate to severe/severe stroke were significantly higher in people with pre-stroke alcohol use disorder (aOR: 1.719, 95% CI: 1.385–2.133), bipolar disorder (aOR: 1.656, 95% CI: 1.281–2.141), and schizophrenia (aOR: 6.884, 95% CI: 3.294–11.492) compared to people who had never had a stroke. Conclusion: The findings in our study suggest that individuals with schizophrenia, bipolar disorder, and alcohol use disorder may be at a higher risk for ischemic stroke and exhibit more severe symptoms. We believe that the first step toward creating beneficial preventative and treatment interventions is determining individuals with schizophrenia, bipolar disorder, or alcohol use disorder, assessing their risk of ischemic stroke, developing more integrated treatments, and closely monitoring the long-term outcome in the event of an ischemic stroke. [ABSTRACT FROM AUTHOR]

Catatonia is a complex neuropsychiatric syndrome, occurring in the context of different psychiatric and neurodevelopmental disorders, in neurological and medical disorders, and after substance abuse or withdrawal. The relationship between Autism Spectrum Disorder (ASD), Schizophrenia Spectrum Disorders (SSDs) and catatonia has been previously discussed, with the three disorders interpreted as different manifestations of the same underlying brain disorder (the "Iron Triangle"). We discuss in this paper the diagnostic, clinical and therapeutic implications of this complex relationship in an adolescent with ASD, who presented an acute psychotic onset with catatonia, associated with mixed mood symptoms. Second-generation antipsychotics were used to manage psychotic, behavioral and affective symptoms, with worsening of the catatonic symptoms. In this clinical condition, antipsychotics may be useful at the lowest dosages, with increases only in the acute phases, especially when benzodiazepines are ineffective. Mood stabilizers with higher GABAergic effects (such as Valproate and Gabapentin) and Lithium salts may be more useful and well tolerated, given the frequent association of depressive and manic symptoms with mixed features. [ABSTRACT FROM AUTHOR]

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%). [ABSTRACT FROM AUTHOR]

Psychological assessment is an integral aspect of training in graduate school. Developing ecologically integrated and culturally sensitive diagnostic tools to support case conceptualization and differential diagnosis is critical to improving assessment practices. Nevertheless, there is a dearth of research on diagnostic training practices, particularly when integrating the cultural context into an assessment. The current study addressed this gap by evaluating four novel diagnostic tools used to guide doctoral externs (n = 11), including case conceptualization and differential diagnostic tables, an attention-deficit/hyperactivity disorder (ADHD) matrix, and a specific learning disorder (SLD) in reading matrix. These tools were designed to integrate a range of clinical data from an ecological context. The current study (1) evaluated extern perspectives on the use and benefit of the diagnostic tools in their training and (2) discussed tool adaptation based on extern feedback. Data were analyzed via frequencies of extern responses to items. The results demonstrated high extern acceptability of the diagnostic tools and that the tools supported their ability to integrate the clients' ecological context into the diagnostic process. Adaptations of the tools in response to extern feedback are discussed. These tools can support culturally and ecologically sensitive assessment practices. [ABSTRACT FROM AUTHOR]

The aim of this study was to quantify the multivariate relationships between clinical, cognitive performance, executive functioning, and psychological outcomes in women with fibromyalgia (FMS) using network analyses. Demographic (age, height, weight), clinical (pain history, pain intensity, and related disability), neurocognitive (D2 Attention test, Rey-Osterrieth Complex Figure for visual perception, "Digits D/R/I" tests of the WAIS-IV battery for working memory, the 5-Digit Test for mental inhibition, the Symbol Search for processing speed and the Zoo Test for planning/decision making) and psychological (depressive symptoms, anxiety levels, sleep quality, pain hypervigilance) variables were collected in 129 women with FMS and 111 healthy women. Network analyses were conducted separately for each group to quantify the adjusted correlations between the modeled variables and to assess their centrality indices (i.e., connectivity with other symptoms in the network and their importance in the network). The network identified 74 associations in FMS women and 46 associations in controls with small differences. The strongest correlations in both groups were found between different attention variables: d2_CON with d2_C, d2_O with d_2TR, and d2_CON with d2_TA. The most central variables were d2_TA, d2_C, and d2_CON (highest strength centrality in both groups) and anxiety levels and pain hypervigilance (highest harmonic centrality in FMS women). The strength centrality of the network was stable for women with FMS (CScor0.7: 0.68) but not for healthy women (CScor0.7: 0.28). This study found that attention variables are most relevant within a neurocognitive network and that psychological variables are most important for the treatment of women with FMS. The clinical implications of the current findings, such as the development of treatments targeting these variables, are discussed. [ABSTRACT FROM AUTHOR]

The importance of night sleep for maintaining good physical and cognitive health is well documented as well as its negative changes during aging. Since Mild Cognitive Impairment (MCI) patients bear additional disturbances in their sleep, this study aimed at examining whether there are potential mixed effects of sleep and afternoon time of day (ToD) on the storage, processing, and updating components of working memory (WM) capacity in older adults with MCI. In particular, the study compared patients' performance in the three working memory components, in two-time conditions: "early in the morning and after night sleep", and "in the afternoon and after many hours since night sleep". The Working Memory Capacity & Updating Task from the R4Alz battery was administered twice to 50 older adults diagnosed with MCI. The repeated measures analysis showed statistically significant higher performance in the morning condition for the working memory updating component (p < 0.001). Based on the findings, it seems that the afternoon ToD condition negatively affects tasks with high cognitive demands such as the WM updating task in MCI patients. These findings could determine the optimal timing for cognitive rehabilitation programs for MCI patients and the necessary sleep duration when they are engaged in cognitively demanding daily activities. [ABSTRACT FROM AUTHOR]

Background: Non-suicidal self-injury (NSSI) and Feeding or Eating Disorders (FEDs) often coexist during adolescence with reciprocal influences on their clinical picture. The present study aimed to identify differences and similarities in the clinical presentation of young patients with both conditions compared to those with the two non-comorbid disorders. Methods: We consecutively recruited forty-five female patients aged between 11 and 18 at our third-level hospital and subdivided them into three groups (NSSI: n = 15; FED: n = 15; NSSI + FED: n = 15). Patients underwent a full clinical assessment. Results: Based on our results, the NSSI + FED group was characterized by higher rates of binging/purging behaviors, greater prevalence of Cyclothymic Disorder, and a more severe clinical presentation compared to the non-comorbid groups. Moreover, higher levels of suicidal ideation were found in the NSSI + FED group. Pharmacological treatment patterns also differed, with SSRI being prescribed more frequently to NSSI + FED patients while mood stabilizers were prescribed more frequently to NSSI ones. A Principal Component Analysis identified four main dimensions: "Body Image" impairment was more pronounced in NSSI + FED patients, indicating negative attitudes towards their own body; "Metacognition" deficits were higher in NSSI than FED. Conclusions: The present study underscores distinctive clinical features in patients with comorbid NSSI and FED, emphasizing the urgent need for tailored intervention strategies focusing on specific symptom domains. [ABSTRACT FROM AUTHOR]

Background/Objectives: Children with autism spectrum disorder (ASD) and/or attention deficit hyperactivity disorder (ADHD) exhibit more executive function (EF) deficits compared to typically developing (TD) peers. EF deficits are linked to various impairments in daily functioning and increased parental stress. The first aim of the present study is to investigate EFs in children with ASD and ADHD compared to their TD peers. The second aim is to explore profiles of executive functions in children with ASD and ADHD and, finally, to determine the differences of EF profiles in relation to parental stress and children's functional impairments. Methods: The sample comprised 30 TD children, 47 children with ASD, and 34 children with ADHD, aged 8 to 12 years. Parents completed questionnaires of parenting stress, and children's social and daily-life functioning. Parents and teachers reported information about children's EF. Results: The results indicated significantly greater impairment of EFs in the clinical groups compared to the TD group. Moreover, three distinct clusters of functioning were identified based on the severity of reported EF difficulties. The significant findings showed that children with more severe EF profiles were associated with greater daily impairment and higher levels of perceived parental stress. Conclusions: Given the impact of EF deficits on the lives of children with ASD and ADHD and their families, it is crucial that studies like this enhance our understanding and inspire future interventions aimed at improving executive functions in children with ASD and ADHD. Such interventions could help reduce parental stress and improve daily functioning. [ABSTRACT FROM AUTHOR]

The widespread use of novel psychoactive substances (NPSs)—defined as new narcotic or psychotropic agents not classified under the Single Convention on Narcotic Drugs of 1961 or the Convention on Psychotropic Substances of 1971—poses a significant challenge to contemporary mental health paradigms due to their impact on psychiatric disorders. This study revisits and expands upon the theory of mental automatism as proposed by Gaëtan Gatian de Clérambault, aiming to elucidate the psychopathological mechanisms underlying substance-induced psychoses (SIP) and their distinction from non-induced psychoses (schizophrenia and related disorders). Through a phenomenological and clinical investigation, we explore the relevance of mental automatism in the development of toxic psychoses, drawing upon the historical and contemporary literature. This research highlights the psychopathological distinctions between induced and non-induced psychoses and the transition mechanisms from acute to chronic psychosis states. De Clérambault's theory, supplemented by Janet, Jackson, and Bonhoeffer's contributions, provides a foundational framework for understanding the genesis of SIP. Our findings suggest that NPS consumption, particularly among adolescents and psychiatric patients, significantly correlates with increased risks of SIP, marked by a transition to chronicity influenced by biological lesions triggered by substance use. Furthermore, we propose a comprehensive framework for SIP, integrating mental automatism, psychopathological distinctions, and transition mechanisms. This framework aims to refine diagnostic criteria and therapeutic approaches, addressing gaps in clinical practice and research. The study underscores the need for a nuanced understanding of SIP, advocating for a paradigm shift in psychiatric assessment and treatment approaches to better address the complexities of substance-induced mental health disorders. [ABSTRACT FROM AUTHOR]

This study aims to determine whether it can distinguish odontogenic keratocyst (OKC) and simple bone cyst (SBC) based solely on preoperative panoramic radiographs through a deep learning algorithm. (1) Methods: We conducted a retrospective analysis of patient data from January 2018 to December 2022 at Pusan National University Dental Hospital. This study included 63 cases of OKC confirmed by histological examination after surgical excision and 125 cases of SBC that underwent surgical curettage. All panoramic radiographs were obtained utilizing the Proline XC system (Planmeca Co., Helsinki, Finland), which already had diagnostic data on them. The panoramic images were cut into 299 × 299 cropped sizes and divided into 80% training and 20% validation data sets for 5-fold cross-validation. Inception-ResNet-V2 system was adopted to train for OKC and SBC discrimination. (2) Results: The classification network for diagnostic performance evaluation achieved 0.829 accuracy, 0.800 precision, 0.615 recall, and a 0.695 F1 score. (4) Conclusions: The deep learning algorithm demonstrated notable accuracy in distinguishing OKC from SBC, facilitated by CAM visualization. This progress is expected to become an essential resource for clinicians, improving diagnostic and treatment outcomes. [ABSTRACT FROM AUTHOR]

The current diagnosis of attention deficit hyperactivity disorder (ADHD) is based on history, clinical observation, and behavioral tests. There is a high demand to find biomarkers for the diagnosis of ADHD. The aim of this study is to analyze the serum profiles of several biomarkers, including homocysteine (Hcy), vitamin B12, vitamin D, ferritin, and iron, in a cohort of 133 male subjects (6.5–12.5 years), including 67 individuals with an ADHD diagnosis based on DSM-V criteria and 66 age-matched healthy boys (healthy controls, HC). Assessments for ADHD included the Iowa Conners' Teacher Rating Scale (CPRS) and the ADHDT test, as well as cognitive assessments using the Wechsler Intelligence Scale for Children-Revised (WISC-R) and the TROG-2 language comprehension test. Hcy and iron were quantified using spectrophotometry, while vitamin B12 and total 25-hydroxy vitamin D levels were determined using an electrochemiluminescence immunoassay (ECLIA) and ferritin was measured using a particle-enhanced immunoturbidimetric assay. The results showed significantly increased Hcy levels and decreased vitamin B12 levels in ADHD patients compared to HCs. Multiple logistic regression analysis indicated that Hcy is a potential prognostic indicator for ADHD. These results suggest that elevated homocysteine and decreased vitamin B12 may serve as markers for the diagnosis and prognosis of ADHD. [ABSTRACT FROM AUTHOR]

Two neurodevelopmental conditions, autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), have been associated with executive function (EF) impairments but the specificity of their impairments is still controversial. The present meta-analysis aimed to identify the differences in EF profiles of ASD, ADHD, and ASD+ADHD in relation to a control group of individuals with typical development (TD) and to understand whether the EF performance could change depending upon the type of measure used to assess EF (performance tests vs. questionnaires). Results from 36 eligible studies revealed that ADHD and ASD showed more difficulties than the TD group in tests and, particularly, in questionnaires. No significant differences in the EF profile emerged between ASD and ADHD when assessed through neuropsychological tests (d = 0.02), while significant differences emerged when assessed through questionnaires, with ADHD having higher ratings than ASD (d = −0.34). EF questionnaires and neuropsychological tests may catch two different constructs of EF, with the former being more predictive of everyday life EF impairments. The comparison between the double diagnosis group (ADHD+ASD) and the clinical groups pointed out that the former has a more similar EF profile to the ADHD-alone one and that it shows more difficulties than ASD-alone. [ABSTRACT FROM AUTHOR]

This study aims to identify the sensory characteristics of children with both developmental disabilities such as ASD and ADHD and a history of child maltreatment (DM group), children with developmental disabilities (DD group), and typically developed children (TD group). The sensory characteristics of 24 children were assessed through virtual reality and self-administered questionnaires. The results elucidated that the duration of gazing at the "teacher" (60–75 s) was significantly different with the DM group's gaze being shorter than the DD group's gaze (p = 0.042). The duration of the "others" gaze (45–60 s) was also significantly different with the DM group's gaze being longer than the DD and TD groups' gaze (p = 0.018; p = 0.030). Additionally, the scores for tactile sensitivity, taste/smell sensitivity, under-responsive/seeks sensation, and the total short-term sensory profile were significantly different between the DM-DD and DD-TD groups. The scores of the heart rate perception test and Multidimensional Assessment of Interoceptive Awareness were significantly lower in the DM and DD groups. In conclusion, children who have experienced trauma and developmental disabilities may have different sensory characteristics than children with only developmental disabilities or none, implying the need for further research and tailored care. [ABSTRACT FROM AUTHOR]

Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning. [ABSTRACT FROM AUTHOR]

Background—There is no validated dementia screening tool for multi-ethnic Asian populations comprising Chinese, Malays, and Indians. This study aimed to establish the psychometric properties of the Community Screening Instrument for Dementia (CSI-D), Chinese Mini Mental State Examination (CMMSE), and Elderly Cognitive Assessment Questionnaire (ECAQ) in multi-ethnic Singapore. Methods—Participants were randomly drawn from a community-based neurological diseases study of older adults conducted in Singapore, with oversampling to allow similar subject numbers from the three major ethnicities: Chinese, Malay, and Indian. The CSI-D, CMMSE, and ECAQ were administered by trained research nurses using a standardised translated questionnaire in a language the participant was most conversant in. Participants were independently diagnosed as a case/non-case of dementia using the DSM-IV. Results—There were 259 participants (85 Chinese, 85 Malays, and 89 Indians, mean age 70.15 years, 65.4% female, 58.8% had no/minimal formal education); 22.8% (n = 59) had dementia. Correlations between the measures were substantial. All the measures had acceptable overall discriminative abilities. Diagnostic accuracies of the instruments did not differ across the ethnic groups. Effects of education were present in the cognitive screening measures. Conclusions—The CSI-D, CMMSE, and ECAQ are valid dementia instruments in this multi-ethnic Asian setting. [ABSTRACT FROM AUTHOR]

Military personnel in combat face a high risk of developing posttraumatic stress disorder (PTSD). In this study, a protocol-based scoping review was conducted to identify the current status of research on the efficacy of acupuncture for treating combat-related PTSD in military personnel. A literature search was conducted across 14 databases in November 2022, and data from the included studies were collected and descriptively analyzed. A total of eight studies were included. Participants were assessed for core PTSD symptoms using the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders-5 and the Clinician-Administered PTSD Scale, as well as related symptoms, such as sleep issues. Although the efficacy of acupuncture has been substantiated in numerous studies, certain metrics did not exhibit improvement. Auricular acupuncture was the most commonly used treatment (50%) followed by manual acupuncture (25%) and a combination of both (25%). Shenmen and Kidney points were frequently targeted at auricular acupoints. The treatment period varied between 5 days and 2 months. While adverse events were reported in two of the fifty-five patients in the intervention group and in four of the sixty-four patients in the control group in the randomized controlled trial studies, no fatal adverse events were reported. [ABSTRACT FROM AUTHOR]

The diagnosis of avoidant/restrictive food intake disorder (ARFID) was added to the diagnostic and statistical manual of mental disorders (DSM-5) just 10 years ago. This disorder consists of the failure to meet one's nutritional and/or energy needs, which may result in significant weight loss, significant nutritional deficit or functioning dependent on enteral nutrition or oral supplements. In children with this disorder, development is often problematic, and there is also marked interference with psychosocial functioning at all ages. The causes leading to food avoidance in these patients may be related to a lack of interest, to the sensory properties of the food or to the possible adverse consequences associated with it. Given the multitude of aspects involved in this disorder and the impact it has especially on younger patients, more and more studies are addressing treatments and related benefits and/or complications. A narrative review of currently published studies was performed for articles published before 5 March 2023 on therapeutic interventions in patients with ARFID. Because of the large number of results obtained, this review was conducted only via PubMed in order to analyze and discuss children and adolescent ARFID treatments reported in literature. The treatments most often referred to in the literature are cognitive behavioral therapy, family-based therapy and pharmacological treatment. All the data on these treatments are promising. However, due to the recent introduction of this disorder and the limited data still available, a multidisciplinary approach seems to be the best option. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading cause of cancer-related deaths globally. Early detection is crucial for improving patient survival rates. Deep learning (DL) has shown promise in the medical field, but its accuracy must be evaluated, particularly in the context of lung cancer classification. In this study, we conducted uncertainty analysis on various frequently used DL architectures, including Baresnet, to assess the uncertainties in the classification results. This study focuses on the use of deep learning for the classification of lung cancer, which is a critical aspect of improving patient survival rates. The study evaluates the accuracy of various deep learning architectures, including Baresnet, and incorporates uncertainty quantification to assess the level of uncertainty in the classification results. The study presents a novel automatic tumor classification system for lung cancer based on CT images, which achieves a classification accuracy of 97.19% with an uncertainty quantification. The results demonstrate the potential of deep learning in lung cancer classification and highlight the importance of uncertainty quantification in improving the accuracy of classification results. This study's novelty lies in the incorporation of uncertainty quantification in deep learning for lung cancer classification, which can lead to more reliable and accurate diagnoses in clinical settings. [ABSTRACT FROM AUTHOR]

The quality of family interactions may be a critical factor for restrictive eating disorders (REDs). Adolescent patients with RED have interpersonal problems that can be inferred by observing their behaviours during family interactions. To date, the assessment of the association among RED severity, interpersonal problems, and patients' interactive behaviours in the family is partially explored. This cross-sectional study aimed to explore how adolescent patients' interactive behaviours observed during the Lausanne Trilogue Play—clinical version (LTPc) were associated with both RED severity and interpersonal problems. Sixty adolescent patients completed the EDI-3 questionnaire to assess RED severity using the Eating Disorder Risk Composite (EDRC) and Interpersonal Problems Composite (IPC) subscales. Moreover, patients and their parents took part in the LTPc, and patients' interactive behaviours were coded as participation, organization, focal attention, and affective contact in all the LTPc four phases. A significant association emerged between patients' interactive behaviours during the LTPc triadic phase and both EDRC and IPC. Better patients' organization and affective contact significantly correlated with lower RED severity and fewer interpersonal problems. These findings suggest that investigating the quality of family relationships and patients' interactive behaviours may contribute to better identifying adolescent patients at risk for more severe conditions. [ABSTRACT FROM AUTHOR]

Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided. [ABSTRACT FROM AUTHOR]

Physical conditions in children and adolescents are often under reported during mainstream school years and may underlie mental health disorders. Additionally, comparisons between younger and older schoolchildren may shed light on developmental differences regarding the way in which physical conditions translate into conduct problems. The aim of the current study was to examine the incidence of psychosomatic complaints (PSC) in young and older adolescent boys and girls who also report conduct problems. A total of 3132 Swedish adolescents (age range 15–18 years, 47% boys) completed the Uppsala Life and Health Cross-Sectional Survey (LHS) at school. The LHS question scores were categorised by two researchers who independently identified questions that aligned with DSM-5 conduct disorder (CD) criteria and PSC. MANOVA assessed the effects of PSC, age, and gender on scores that aligned with the DSM criteria for CD. The main effects of gender, age, and PSC on the conduct problem scores were observed. Adolescents with higher PSC scores had higher conduct problem scores. Boys had higher serious violation of rules scores than girls, particularly older boys with higher PSC scores. Psychosomatic complaints could be a useful objective identifier for children and adolescents at risk of developing conduct disorders. This may be especially relevant when a reliance on a child's self-reporting of their behavior may not help to prevent a long-term disturbance to their quality of life. [ABSTRACT FROM AUTHOR]

Burnout is generally acknowledged by researchers, clinicians, and the public as a pervasive occupational difficulty. Despite this widespread recognition, longstanding debates remain within the scientific community regarding its definition and the appropriateness of classifying burnout as its own pathological entity. The current review seeks to address whether burnout should (or could) be characterized as a distinctive mental disorder to shed light on this debate. After briefly reviewing the history, theoretical underpinnings, and measurement of burnout, we more systematically consider the current evidence for and against its classification as a mental disorder within existing diagnostic systems. Stemming from a lack of conceptual clarity, the current state of burnout research remains, unfortunately, largely circular and riddled with measurement issues. As a result, information regarding the unique biopsychosocial etiology, diagnostic features, differential diagnostic criteria, and prevalence rates of burnout are still lacking. Therefore, we conclude that it would be inappropriate, if not premature, to introduce burnout as a distinct mental disorder within any existing diagnostic classification system. We argue, however, that it would be equally premature to discard burnout as a psychologically relevant phenomenon and that current evidence does support its relevance as an important occupational syndrome. We finally offer several avenues for future research, calling for cross-national collaboration to clarify conceptual and measurement issues while avoiding the reification of outdated definitions. In doing so, we hope that it one day becomes possible to more systematically re-assess the relevance of burnout as a distinctive diagnostic category. [ABSTRACT FROM AUTHOR]

Internet gaming disorder (IGD) has been included in the 2013 Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a condition in need of further study, and gaming disorder was recognized by the World Health Organization as a mental disorder in the International Classification of Disease (ICD-11) of 2018. IGD has different characteristics in the two sexes and is more prevalent in males than females. However, even if the female gamer population is constantly growing, the majority of available studies analyzed only males, or the data were not analyzed by sex. To better elucidate sex differences in IGD, we selectively reviewed research publications that evaluated IGD separately for males and females collected in approximately one hundred publications over the past 20 years. The available data in this narrative review indicate that IGD is strongly dimorphic by sex for both its psychological features and the involvement of different brain areas. Impulsivity, low self-control, anxiety, emotion dysregulation, and depression are some of the psychological features associated with IGD that show a sex dimorphism. At the same time, IGD and its psychological alterations are strongly correlated to dimorphic functional characteristics in relevant brain areas, as evidenced by fMRI. More research is needed to better understand sex differences in IGD. Animal models could help to elucidate the neurological basis of this disorder. [ABSTRACT FROM AUTHOR]

The Child Behavior Checklist 1.5–5 (CBCL 1.5–5) is applied to identify emotional and behavioral problems on children with developmental disabilities (e.g., autism spectrum disorder [ASD] and developmental delays [DD]). To understand whether there are variations between these two groups on CBCL DSM-oriented scales, we took two invariance analyses on 443 children (228 children with ASD). The first analysis used measurement invariance and multiple-group factor analysis on the test structure. The second analysis used item-level analysis, i.e., differential item functioning (DIF), to discover whether group memberships responded differently on some items even though underlying trait levels were the same. It was discovered that, on the test structure, the Anxiety Problems scale did not achieve metric invariance. The other scales achieved metric invariance; DIF analyses further revealed that there were items that functioned differently across subscales. These DIF items were mostly about children's reactions to the surrounding environment. Our findings provide implications for clinicians to use CBCL DSM-oriented scales on differentiating children with ASD and children with DD. In addition, researchers need to be mindful about how items were responded differently, even though there were no mean differences on the surface. [ABSTRACT FROM AUTHOR]

Background. Developmental Language Disorder (DLD) is frequent in childhood and may have long-term sequelae. By employing an evidence-based approach, this scoping review aims at identifying (a) early predictors of DLD; (b) the optimal age range for the use of screening and diagnostic tools; (c) effective diagnostic tools in preschool children. Methods. We considered systematic reviews, meta-analyses, and primary observational studies with control groups on predictive, sensitivity and specificity values of screening and diagnostic tools and psycholinguistic measures for the assessment of DLD in preschool children. We identified 37 studies, consisting of 10 systematic reviews and 27 primary studies. Results. Delay in gesture production, receptive and/or expressive vocabulary, syntactic comprehension, or word combination up to 30 months emerged as early predictors of DLD, a family history of DLD appeared to be a major risk factor, and low socioeconomic status and environmental input were reported as risk factors with lower predictive power. Optimal time for screening is suggested between age 2 and 3, for diagnosis around age 4. Because of the high variability of sensitivity and specificity values, joint use of standardized and psycholinguistic measures is suggested to increase diagnostic accuracy. Conclusions. Monitoring risk situations and employing caregivers' reports, clinical assessment and multiple linguistic measures are fundamental for an early identification of DLD and timely interventions. [ABSTRACT FROM AUTHOR]

Lean Thinking is a methodology based on improving the efficiency of productive processes by removing non-value-added issues. This methodology was firstly applied in the manufacturing industry, but it has also been applied to many service companies, bringing very good results. In the last decade, some works have tried to research the adaptation of Lean principles and practices to teaching, especially in technology and other STEAM subjects. In this sense, the aim of this work is to deepen this new trend by establishing what issues are non-value-added (waste) ones in education and classifying these kinds of waste in order to be able to analyze how to eliminate them. For this purpose, we adapt the classification made in other kinds of processes and extend other authors' findings regarding this topic. [ABSTRACT FROM AUTHOR]